To learn high-level representation from data, the work [1]} proposed a combination of multi-layer AEs with maximum noise fraction which reduces the spectral dimensionality of HSI, while a softmax logistic regression classifier is employed for HSIC. The study reported in [2]} combined multi-manifold learning framework proposed by [3]} with Counteractive Autoencoder [4]} for improved unsupervised HSIC. The work [5]} jointly exploited spectral-spatial features of HSI through an unsupervised feature extracting framework composed of recursive autoencoders (RAE) network. It extracts the features from the neighborhood of the target pixel and weights are assigned based on the spectral similarity between target and neighboring pixels. A two-stream DNN with a class-specific fusion scheme was introduced in [6]} which learns the fusion weights adaptively. One stream composed of stacked denoising auto-encoder is used to extract spectral features and the second stream is implemented to extract spatial information using Convolutional Neural Network (CNN), while final classification is performed by fusing the class prediction scores obtained from the classification results of both streams.
